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Machine learning drives Grab’s Open Traffic initiative

OVER the past few years, the media has focused a lot on how companies the likes of Uber, Grab and Airbnb have exploited the so-called ‘sharing economy,’ highlighting the fact that they don’t own assets but are still able to dominate the transportation and accommodation industries respectively.

But as these companies hog the headlines insofar as their valuations and growth rates are concerned, unbeknownst to many, they are also highly-tuned, data intensive-driven companies.

Being data driven means that they are able to pour resources into helping to solve one of society’s most challenging problems – that of traffic congestions in the countries they operate in.

Last April, Southeast Asian-based (SEA) Grab launched the OpenTraffic initiative in Malaysia, an effort aimed at providing traffic data from Grab’s GPS data streams to address traffic congestion and improve road safety in major Malaysian cities. The initiative was done in collaboration with the Malaysia Digital Economy Corporation Sdn Bhd (MDEC) and the World Bank Group.

Essentially, OpenTraffic provides Malaysia’s traffic management agencies and city planners access to an open dataset to better manage traffic flow and make investment decisions on local transport infrastructure.

The initiative is provided at no cost to governments via an open data licence. In practice, OpenTraffic translates Grab drivers’ GPS data into anonymised traffic data, to map traffic speeds on roads for analysing traffic congestion peak patterns and travel times.

The platform is designed to assist traffic management agencies in easing traffic flow, particularly within dense urban areas. Local government agencies can use the data to enhance existing traffic management systems such as optimising traffic light control and coordination.

Head of engineering for Grab Ditesh Gathani (pic, above) said the Open Traffic initiative started about two-and-a-half years ago in the Philippines.

At that time, the local authorities in the Philippines were facing a quandary as to how to manage their growing traffic woes, and it turned to the World Bank to help them solve these challenges, Ditesh said.

According to the World Bank, congestion in metropolitan Manila costs the economy more than US$60 million (RM237.14 million) per day, and it is not atypical to spend more than two hours to travel 8km during the evening commute there.

Conventional methods of collecting traffic data were either too capital intensive or too slow and inaccurate. This is when the World Bank turned to ride-hailing companies such as Grab and others such as Easy Taxi and Le.Taxi.

Combined together, these three ride-sharing companies cover more than 30 countries and millions of customers and are working with the World Bank and its partners to make traffic data derived from their drivers’ GPS streams available to the public through an open data licence.

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